At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are now building services and agentic tools that provide AI coding agents and end users with deeper context about codebases. Our code retrieval service already delivers meaningful improvements in agent speed and task performance, and we aim to push this further – extracting richer insights than snippets alone at the scale of several hundred thousand repositories. 

We are looking for an AI Engineer who can design and implement agentic tools from scratch, bring them to the end users, and make coding with agents smarter, faster, and more reliable.

In this role, you will:

• Translate product needs into agentic tools and services and ship them to production.
• Scale prototypes to large numbers of users.
• Improve and test our semantic search service.
• Build evaluation pipelines for code retrieval and coding task performance.

We’ll be happy to have you on our team if you have:

• At least five years of hands-on NLP experience.
• Experience in information retrieval systems.
• Strong experience in LLM prompt and tool engineering, as well as with AI agents.
• Solid programming skills in Python and/or Kotlin (and the willingness to learn the language you’re unfamiliar with).
• Experience building and operating production-grade software systems.

We’d be especially thrilled if you have:

• Experience developing in Kotlin or Java.
• Previously worked with code intelligence (e.g. AST, symbol graphs).

#LI-DNI

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior ML Researcher to develop the core methods that make Spectrum possible – novel approaches to temporal ontology extraction, contradiction detection, and semantic alignment across heterogeneous software artifacts. You will help define and execute the research agenda, while also collaborating with JetBrains Research and external academic advisors.

Your responsibilities will include:

• Developing methods for LLM-based ontology construction from code, documentation, and issue trackers, including contradiction detection and reasoning over the resulting knowledge graph.
• Creating datasets, metrics, and benchmarks that drive measurable improvements across the system.
• Prototyping and validating research ideas, then collaborating with ML and software engineers to bring them to production.
• Collaborating with JetBrains Research and external academic advisors to shape our research agenda.
• Publishing research findings at top venues and representing Spectrum in the research community.

We expect you to have:

• A PhD (or equivalent research experience) in NLP, knowledge graphs, ontology learning, information extraction, or a closely related field.
• A strong publication record in at least one of the following areas: knowledge graph construction, ontology learning, information extraction, or NLP.
• Experience applying LLMs to structured knowledge extraction tasks.
• Experience designing and running rigorous experiments, including hypothesis formulation, ablation studies, and statistical evaluation.
• Strong Python and PyTorch skills, with the ability to implement research ideas from scratch.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontology engineering, semantic web technologies (OWL, RDF, SPARQL), or ontology alignment.
• A background in code analysis, developer tools, or software engineering research.
• Experience with knowledge graph embedding methods or graph neural networks.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• Actively contributed to relevant open-source projects.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• Full support for publishing ML research and attending industry conferences.
• Collaboration with JetBrains Research and external academic advisors in the knowledge graph and ontology learning community.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• A rare applied research role where your methods ship in a real product used by software teams.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

About the role:

ABBYY Vantage is our flagship Document AI platform – a cloud-native, microservices-based product that combines purpose-built AI, OCR, NLP, and direct LLM integration to help enterprise customers extract, classify, and act on data from any document. Vantage 3.0, our latest release, introduces native Azure OpenAI integration, enterprise-grade compliance and redaction, a real-time analytics dashboard, and a growing library of pre-configured AI skills. Over 10,000 customers trust ABBYY – including Fortune 500 companies like DHL, Johnson & Johnson, FDA, PwC, KeyBank, Spotify, and H&R Block – to automate their most complex document workflows.

We are looking for a Staff Software Engineer to serve as a senior technical leader on the Vantage platform. You will drive the design and delivery of complex, cross-cutting capabilities – spanning document AI pipelines, LLM integrations, compliance infrastructure, analytics, and platform extensibility. Beyond your own code, you will act as a technical multiplier: influencing architectural decisions, setting quality standards, and developing the engineers around you. This is a role for someone who leads through depth, judgment, and impact.

Job Responsibilities:

• Technical Leadership: Drive the technical design and delivery of significant Vantage platform capabilities, from architecture and API contracts through deployment, monitoring, and iteration.
• Quality & Standards: Define and uphold engineering standards for the Vantage codebase – covering code quality, service reliability, performance, security, and compliance – and drive their adoption across teams.
• Strategic Collaboration: Act as a senior technical voice in cross-functional planning with product management, ML engineering, cloud architecture, and engineering leadership; translate business priorities into actionable technical roadmaps.
• Platform Improvement: Lead initiatives to improve Vantage platform health at scale: service decomposition, observability, CI/CD maturity, and developer productivity.
• Operational Excellence: Own high-severity incident response for Vantage cloud services; drive post-mortems and systemic reliability improvements.
• Engineering Mentorship: Mentor Senior and Principal Engineers on the Vantage team; build a culture of technical rigor, shared ownership, and continuous learning.
• Technical Innovation: Evaluate and champion adoption of new technologies – from GenAI model integrations to cloud-native tooling – that strengthen ABBYY's Document AI differentiation.

Job Requirements:

• 10–13 years of professional software engineering experience with deep expertise in C# .NET and C++, willing to work with both languages
• Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
• Strong command of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
• Demonstrated ability to mentor Senior and Mid-level Engineers and partner with Engineering Managers on technical direction
• Experience leading technical design reviews and influencing architectural decisions across team and product boundaries

Desired Qualifications

• Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
• Track record of delivering production-grade systems that meet enterprise compliance, security, and reliability standards
• Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
• Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
• Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures

About the role:

ABBYY Vantage is our flagship Document AI platform – a cloud-native, microservices-based product that combines purpose-built AI, OCR, NLP, and direct LLM integration to help enterprise customers extract, classify, and act on data from any document. Vantage 3.0, our latest release, introduces native Azure OpenAI integration, enterprise-grade compliance and redaction, a real-time analytics dashboard, and a growing library of pre-configured AI skills. Over 10,000 customers trust ABBYY – including Fortune 500 companies like DHL, Johnson & Johnson, FDA, PwC, KeyBank, Spotify, and H&R Block – to automate their most complex document workflows.

We are looking for a Senior Software Engineer to join the Vantage engineering team. You will be a hands-on contributor building and evolving the services that power Vantage's document AI capabilities – from OCR pipelines and skill training workflows to API integrations and analytics infrastructure. You bring strong engineering fundamentals, a quality-first mindset, and a drive to deliver reliable, scalable software in a cloud-native environment.

Job Responsibilities:

• End-to-End Development: Design, develop, test, and deploy features and services within the Vantage platform, contributing to areas such as document processing pipelines, AI skill management, cloud infrastructure, and API layers.
• Code Quality: Write clean, maintainable, well-tested code; actively participate in code reviews and champion engineering best practices across your team.
• Cross-Functional Collaboration: Work closely with product managers, UX designers, ML engineers, and fellow software engineers to translate Vantage feature requirements into well-architected solutions.
• Continuous Improvement: Identify and surface improvements to existing Vantage services, tooling, and delivery workflows; contribute to reducing technical debt and increasing engineering velocity.
• System Reliability: Monitor the health and performance of Vantage cloud services; investigate and resolve production incidents in a timely manner.
• Technical Growth: Stay current with advances in Document AI, cloud-native patterns, and GenAI integration approaches that could enhance the Vantage platform.

Job Requirements:

• 5–9 years of professional software engineering experience with deep expertise in C# .NET and C++, willing to work with both languages
• Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
• Good understanding of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
• Demonstrated track record of delivering high-quality, testable code
• Strong analytical and debugging skills with the ability to investigate complex issues across distributed systems

Desired Qualifications

• Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
• Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
• Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
• Familiarity with OCR, NLP and computer vision technologies
• Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

The JetBrains Research team explores ways to use machine learning techniques and agentic approaches to help developers and enhance software development processes. Our work aims to improve the state of ML for code by turning the latest academic advances into practical applications.

Our team is looking for an ML Engineer to participate in a variety of projects in areas such as code completion and generation, AI agents development, and test generation. Though we don’t expect the candidate to have experience in all the tasks we work on, we are looking for someone excited to take on the challenge of working in a diverse set of contexts.

In this role, you will: 

• Design and prototype machine learning solutions to improve the software development workflow.
• Apply existing open-source models or train custom ones as needed.
• Build training and evaluation pipelines that support fast iteration and ensure reproducibility.
• Stay up to date with the latest advancements in applying machine learning to source code.
• Collaborate with colleagues and provide mentorship when appropriate.

We’ll be happy to have you on our team if you: 

• Have experience designing machine learning systems from scratch.
• Have a background in natural language processing or code modeling.
• Have a proven track record of delivering machine learning projects.
• Know how to design reproducible ML experiments.
• Enjoy working on projects with high uncertainty.
• Are proficient in Python, Java, or Kotlin.
• Have hands-on experience with modern ML frameworks.

It would be nice if you: 

• Have experience applying machine learning to programming languages.
• Have hands-on experience building and improving AI agents.
• Have experience in A/B testing and general knowledge of statistics
• Have worked with large-scale data processing pipelines

#LI-KP1